自适应遗传算法在服装生产流水线平衡问题中的应用

将自适应遗传算法应用于服装生产流水线中,利用自适应遗传算法能够自动给出比较合适的交叉概率、和变异概率,显著提高了搜索效率,同时改进了遗传算法的搜索能力的特点,解决并行制造中的流水线平衡问题。针对某款运动上衣的生产工艺进行仿真,结果表明了该算法的有效性。     

RFID技术在服装单件流生产流水线的应用研究

针对服装单件流生产流水线的常见不足,将RFID技术应用于服装生产过程,设计了一整套软硬件系统。该系统能实现服装生产过程中诸如产品规格、数量、合格率、生产节拍、操作人员等参数的实时监控,提高服装生产企业的生产效率与信息化管理水平。     

基于改进FABLE算法的流水线平衡问题的实现

在一种深度优先的分枝定界算法基础上,研究了实际生产约束条件下大规模流水线生产平衡问题,提出了合并规则、排序规则、可行性规则和替代规则以满足约束条件及减少搜索空间,给出了求解平衡问题新的启发式优化算法。应用此算法解决了某复印机厂实际流水线的平衡问题。     

遗传算法在企业车间生产调度中的应用

车间生产调度是企业生产中的一个重要课题,如何使生产过程中的调度最优化,是一个长期研究的问题,本文提出的用遗传算法解决企业生产调度问题,并通过实验证明了该方法的有效性.     

遗传算法在铜板带生产调度中的应用研究

根据铜板带生产流程的特点,考虑到铜板带生产调度的多目标性,建立基于遗传算法带加权系数的铜板带生产调度模型;然后应用遗传算法对该模型进行调度并获取近似最优解。该算法采用矩阵编码、混合选择策略和三个体交叉方法,使其在有限的步骤内,找出所有满足约束条件的最优或次优解。仿真结果表明该算法可以有效地解决铜板带生产调度问题,具有实用性。     

一类含同工件流水线调度问题的优化研究

流水线调度问题是具有很强工程背景的典型NP完全问题,当其含有同工件时,批量和排序的相关性使得问题的求解更为复杂。文章有机结合模拟退火的概率突跳性和遗传算法的并行搜索结构,提出了处理一类含同工件流水线调度问题的混合优化策略。算法不仅能够动态缩小搜索空间以提高搜索效率,而且在保优策略的基础上利用重升温技术来增强克服陷入局部极小的能力,其有效性和快速性通过仿真得到了验证。     

应用遗传算法解决装配线平衡问题

文章针对装配线平衡问题,提出了一种周期性自适应交换、变异遗传算法,通过实验求解表明,该算法是解决装配线问题的有效算法,很好地解决了简单遗传算法容易早熟收敛的问题,大大改善了简单遗传算法的性能。     

遗传算法在列车占线问题中的应用

列车占线是铁路车站运转中一个复杂的组合优化问题，应用遗传算法，建立了描述列车占线问题的染色体结构和适应度函数，设计了问题的遗传操作，进行了沈阳北站列车占线问题的仿真实验，这一工作，对于建立铁路车站占线自动化系统具有重要意义。     

遗传算法在车辆调度优化问题中的研究

车辆调度优化问题是一个有约束的组合优化问题,属于NP难题（Nondeterministic Polynomial Problem）。随着问题输入规模的扩大,求解时间呈几何级数上升,传统的优化算法本身存在着过早收敛于局部值的问题。针对这一问题在染色体编码、算子的自适应机制和约束的处理等方面对标准遗传算法进行了改进。测试结果表明,该算法提高了优化算法的质量和搜索效率,具有良好的效果。     

应用遗传算法求解第一类装配线平衡问题

装配线的负荷平衡对生产企业非常重要。负荷平衡的装配线可以使各工作站空闲时间减少,并使工作站之间负荷均匀,以保证装配线时间损失率最低。针对第一类装配线平衡问题,首先对该问题进行了分析,在此基础上,建立了问题的数学模型。进而给出求解该问题的遗传算法,设计了该遗传算法的编码模式和译码方法,构造了适应函数,确定了初始种群的选取方法和种群的选择机制,并给出了种群的交叉和变异方法。通过求解某实例的装配线平衡问题,显示了这种算法的有效性。     

应用遗传算法求解第一类装配线平衡问题

装配线的负荷平衡对生产企业非常重要。负荷平衡的装配线可以使各工作站空闲时间减少,并使工作站之间负荷均匀,以保证装配线时间损失率最低。针对第一类装配线平衡问题,首先对该问题进行了分析,在此基础上,建立了问题的数学模型。进而给出求解该问题的遗传算法,设计了该遗传算法的编码模式和译码方法,构造了适应函数,确定了初始种群的选取方法和种群的选择机制,并给出了种群的交叉和变异方法。通过求解某实例的装配线平衡问题,显示了这种算法的有效性。     

Assembly Line Balancing Using Differential Evolution Models

There is a growing research interest on the application of evolutionary computation-based techniques in manufacturing optimization due to the fact that this field is associated with a plethora of complex combinatorial optimization problems. Differential evolution (DE), one of the latest developed evolutionary algorithms, has rarely been applied on manufacturing optimization problems (MOPs). A possible reason for the absence of DE from this research field is that DE was introduced as global optimizer over continuous spaces, while most of MOPs are of combinatorial nature with discrete decision variables. DE maintains and evolves floating-point vectors and therefore its application to MOPs that have solutions represented by permutations is not straightforward. This paper investigates the use of DE for the solution of the simple assembly line balancing problem (SALBP), a well-known NP-hard MOP. Two basic formulation types for SALBP are examined, namely type-1 and type-2: the former attempts to minimize the number of workstations required to manufacture a product in an assembly line for a given fixed cycle time; while the latter attempts to minimize the cycle time of the line for a given number of stations. Extensive experiments carried out over public benchmarks test instances estimate the performance of DE approach.     

Model and heuristics for the Assembly Line Worker Integration and Balancing Problem

We propose the Assembly Line Worker Integration and Balancing Problem (ALWIBP), a new assembly line balancing problem arising in lines with conventional and disabled workers. The goal of this problem is to maintain high productivity levels by minimizing the number of workstations needed to reach a given output, while integrating in the assembly line a number of disabled workers. Being able to efficiently manage a heterogeneous workforce is especially important in the current social context where companies are urged to integrate workers with different profiles. In this paper we present mathematical models and heuristic methodologies that can help assembly line managers to cope with this additional complexity. We demonstrate by means of a robust benchmark how this integration can be done with losses of productivity that are much lower than expected.     

基于禁忌搜索遗传混合算法的装配线平衡

针对装配线平衡问题（ALBP）,文中提出了一种禁忌搜索遗传混合算法。在混合算法中,遗传算法部分采用特殊的遗传变异操作算子（双点交叉和移位插入变异）,使算法只在可行作业序列子空间中进行搜索,有效减小了搜索范围,提高了算法运行效率;禁忌搜索部分是在每代遗传操作完成以后,随机选择一些个体进行禁忌搜索操作,来增强算法的搜索能力。最后以经典问题的求解验证了禁忌搜索遗传算法在收敛性能和计算效率上较使用单纯的遗传算法高。     

基于结构式译码遗传算法平衡多人共站装配线

针对多人共站装配线第二类平衡问题,以最小化装配线节拍为主要目标,最小化平行操作工人数为第一辅助目标,最小化工人负荷标准差为第二辅助目标构建数学模型,在此基础上,构建结构式译码遗传算法,其中特别设计了两阶段结构式译码算法,在第一阶段,求解给定作业序列在单人工作站条件下的最优分配方案,第二阶段将方案中的工作站进行合并,以使节拍最小化。通过算例验证算法的有效性和可行性。     

Multi-rule Multi-objective Simulated Annealing Algorithm for Straight and U Type Assembly Line Balancing Problems

The task of balancing of assembly lines is of considerable industrial importance. It consists of assigning operations to workstations in a production line in such a way that (1) no assembly precedence constraint is violated, (2) no workstations in the line takes longer than a predefined cycle time to perform all tasks assigned to it, and (3) as few workstations as possible are needed to perform all the tasks in the set. This paper presents a new multiple objective simulated annealing (SA) algorithm for simple (line) and U type assembly line balancing problems with the aim of maximizing “smoothness index” and maximizing the “line performance” (or minimizing the number of workstations). The proposed algorithm makes use of task assignment rules in constructing feasible solutions. The proposed algorithm is tested and compared with literature test problems. The proposed algorithm found the optimal solutions for each problem in short computational times. A detailed performance analysis of the selected task assignment rules is also given in the paper.     

A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II

Mixed-model assembly lines allow for the simultaneous assembly of a set of similar models of a product, which may be launched in the assembly line in any order and mix. As current markets are characterized by a growing trend for higher product variability, mixed-model assembly lines are preferred over the traditional single-model assembly lines.

This paper presents a mathematical programming model and an iterative genetic algorithm-based procedure for the mixed-model assembly line balancing problem (MALBP) with parallel workstations, in which the goal is to maximise the production rate of the line for a pre-determined number of operators.

The addressed problem accounts for some relevant issues that reflect the operating conditions of real-world assembly lines, like zoning constraints and workload balancing and also allows the decision maker to control the generation of parallel workstations.     

回溯算法在制丝生产线自动排产中的应用

烟草企业面临着市场供给和计划生产的双重挑战,烟草企业制丝生产线自动排产调度越来越受到重视,优化制丝生产调度是提高生产效率的重要手段.论文分析了烟草制丝自动排产的业务流程和质量指标,对制丝生产线的生产段、工艺柜等进行理论分析和科学建模,采用回溯算法实现了制造生产调度自动排产与优化,得到了预期效果.